Assessment of the spatio-temporal pattern of PM2.5 and its driving factors using a land use regression model in Beijing, China

With the acceleration of urbanization and industrialization, atmospheric pollution has become a major issue, restricting the sustainable development of the urban environment. Since 2013, Beijing has been among China's most seriously affected regions in terms of haze pollution. Atmospheric pollution is closely linked to land use, particularly the spatial patterns of green and urban land. Therefore, the quantification of the relationship between fine particulate matter (PM2.5) concentration and its driving factors in Beijing is of considerable significance for environmental management and spatial epidemiological studies. A land use regression (LUR) model was constructed to simulate the spatio-temporal distribution of PM2.5 concentration. In this study, the independent variables (driving factors) included land use, meteorological factors, population, roads, the digital elevation model, and the normalized difference vegetation index. The five models had adjusted R-2 of 0.887, 0.770, 0.742, 0.877, and 0.798, respectively. Land use and meteorological factors were the main factors affecting PM2.5 concentration. The driving factors of land use on a large scale and roads on a small scale had a significant impact on PM2.5 emissions. Beijing's PM2.5 concentrations in 2015 showed clear spatio-temporal characteristics. The highest (lowest) average PM2.5 concentration was recorded in winter (summer). In terms of spatial distribution, PM2.5 concentrations showed a "low in the northwest and high in the southeast" trend. The most polluted areas were mainly distributed in the central city and the southeastern and southwestern regions. The PM2.5 concentration boundary was essentially consistent with the boundary of land use type. Different land use types promoted or inhibited PM2.5 concentrations, with a difference of more than 20 mu g/m(3) PM2.5 between the two land use categories. Thus, PM2.5 concentrations should be controlled by optimizing the spatial and temporal patterns of land use.